import pandas as pd
import numpy as np
import os
os.chdir("/home/shahul/Downloads/Data_Set_Qualifiers/")

import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
plt.style.use('ggplot')

Edta of each mode of transport

Kochi metro

Loading dataframes

path="Mobility/Kochi Metro/KMRL - CSV/"
fares=pd.read_csv(path+"/Fares.csv", encoding = "ISO-8859-1")
routes=pd.read_csv(path+"/Routes.csv", encoding = "ISO-8859-1")
schedules=pd.read_csv(path+"/Schedules.csv", encoding = "ISO-8859-1")
stops=pd.read_csv(path+"/Stops.csv", encoding = "ISO-8859-1")

Basic understandings

fares.shape

(16, 17)

fares.describe()['Aluva']

count    16.000000
mean     33.125000
std      14.008926
min      10.000000
25%      20.000000
50%      35.000000
75%      42.500000
max      50.000000
Name: Aluva, dtype: float64

• Here you can see that there are 16 stops for kochi metro.
• The mean expense is 33INR.
• Min price is 10inr.
• Max price is 50inr.

What is the fare per kilometer?

plt.figure(figsize=(9,7))
fareper=(fares.iloc[:,:2]['Aluva']/schedules['dist_traveled (km)'][0:16]).replace(np.inf,0)
plt.plot(np.arange(0,16),fareper)
plt.xlabel("No of stops")
plt.ylabel("Fare per stop")
plt.savefig("fares.png")
plt.show()

• Its evident from the above graph that as the number of stops user travelled increases,the fare per km decreases

routes.head()

	route_id	route_short_name	route_long_name	route_type	route_color	route_text_color
0	R001	R1	Aluva to Maharaja College	1	00B7F3	0

schedules.shape

(6016, 7)

schedules.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6016 entries, 0 to 6015
Data columns (total 7 columns):
trip_id               6016 non-null object
arrival_time          6016 non-null object
departure_time        6016 non-null object
stop_id               6016 non-null object
stop_sequence         6016 non-null int64
timepoint             6016 non-null int64
dist_traveled (km)    6016 non-null float64
dtypes: float64(1), int64(2), object(4)
memory usage: 329.1+ KB

schedules['arrival_time']=schedules['arrival_time'].apply(lambda x:datetime.strptime(x,"%H:%M:%S"))
schedules['departure_time']=schedules['departure_time'].apply(lambda x:datetime.strptime(x,"%H:%M:%S"))

schedules['delta']=(schedules['departure_time']-schedules['arrival_time']).apply(lambda x:x.seconds)
stop=schedules.groupby("stop_id",as_index=False)['delta'].agg('mean')

plt.figure(figsize=(7,7))
sns.barplot(y=stop['stop_id'],x=stop['delta'])
plt.xlabel('Time in seconds')
plt.title("Average waiting time in each stops")
plt.savefig("avg_wait.png")

Mode=["Ferry","Ferry Vega","Bus","car","walk","Bicycle","metro"]
speed=[14,22,20,28,5,16,35]
co2=[13,20,18,135,0,0,10]
extra_df=pd.DataFrame({"Mode":Mode,"speed":speed,"co2":co2})

extra_df.head()

	Mode	speed	co2
0	Ferry	14	13
1	Ferry Vega	22	20
2	Bus	20	18
3	car	28	135
4	walk	5	0

Speed by each transport

plt.figure(figsize=(8,5))
sns.barplot(y=extra_df['Mode'],x=extra_df['speed'])
plt.xlabel("speed in kmph")
plt.title(' Speed by each transport')
plt.show()
plt.savefig('speed.png')

<Figure size 432x288 with 0 Axes>

Co2 emission by each transport

plt.figure(figsize=(8,5))
sns.barplot(y=extra_df['Mode'],x=extra_df['co2'])
plt.xlabel("CO2 Emission (gm/km/passenger)")
plt.title('Co2 emission by each transport')
plt.show()
plt.savefig('co2.png')

<Figure size 432x288 with 0 Axes>

Geolocations

import plotly
import plotly.graph_objs as go

plotly.offline.init_notebook_mode(connected=False)

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'


data = [
    go.Scattermapbox(
        lat=stops['stop_lat'],
        lon=stops['stop_lon'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=stops['stop_name'],
    )
]





layout = go.Layout(title="metro stations locations",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=dict(
            lat=10.03,
            lon=76.30
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='~/file1.html')

Ferry

path="Mobility/Ferry/Ferry - CSV/"
f_routes=pd.read_csv(path+"Routes.csv").copy()
f_schedules=pd.read_csv(path+"Schedules.csv").copy()
f_stops=pd.read_csv(path+"Stop_Locations.csv").copy()
f_trips=pd.read_csv(path+"Trips.csv").copy()

Basic ideas

f_routes.shape

(43, 2)

f_routes.head(5)

	route_id	Route Name
0	FOU01	Vytilla - Kakkanad
1	FOD01	Kakkanad - Vytilla
2	FOU02	Ernakulam - Varappuzha
3	FOD02	Varappuzha - Ernakulam
4	FOU03	Varappuzha - Chitoor

len(f_routes['Route Name'].unique())

43

• There are 43 service routes for ferry

f_schedules.head()

	route_id	trip_id	stop_id	arrival_time	departure_time	stop_sequence
0	FOU01	TRIP0001	FS024	07:00:00	07:00:00	1
1	FOU01	TRIP0001	FS005	07:30:00	07:30:00	2
2	FOU01	TRIP0001	FS011	08:00:00	08:00:00	3
3	FOD01	TRIP0002	FS011	07:30:00	07:30:00	1
4	FOD01	TRIP0002	FS005	08:00:00	08:00:00	2

f_stops.head()

	stop_id	stop_name	stop_lat	stop_lon
0	FS002	Chittoor	10.038325	76.26991
1	FS003	Embarkation	9.970072	76.26221
2	FS004	Ernakulam	9.972357	76.27833
3	FS005	Eroor	9.980063	76.33351
4	FS006	Fort Kochi	9.969376	76.25187

import plotly
import plotly.graph_objs as go

plotly.offline.init_notebook_mode(connected=False)

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'

data = [
    go.Scattermapbox(
        lat=f_stops['stop_lat'],
        lon=f_stops['stop_lon'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=f_stops['stop_name'],
    )
]





layout = go.Layout(title="Ferry stop locations",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=dict(
            lat=9.99,
            lon=76.35
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

f_trips.head()

	route_id	trip_id	direction_id	wheelchair_accessible
0	FOU01	TRIP0001	0	Yes
1	FOD01	TRIP0002	0	Yes
2	FOU01	TRIP0003	0	Yes
3	FOD01	TRIP0004	0	Yes
4	FOU01	TRIP0005	0	Yes

Auto stands

path="Mobility/Auto Stands Locations/"
auto=pd.read_csv(path+"Auto_Stands.csv")

plotly.offline.init_notebook_mode(connected=False)

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'

data = [
    go.Scattermapbox(
        lat=auto['Latitude'],
        lon=auto['Longitude'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=auto['Name'],
    )
]





layout = go.Layout(title="Auto stand locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=dict(
            lat=10.10,
            lon=76.35
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

Non Mobility

path="Non-Mobility/"
accommodation=pd.read_csv(path+"Accommodation.csv")
cultural=pd.read_csv(path+"Cultural.csv")
Gastronomy=pd.read_csv(path+"Gastronomy.csv",encoding= "ISO-8859-1")
Recreation=pd.read_csv(path+"Recreation.csv")
religious=pd.read_csv(path+"Religious_Establishments.csv",encoding= "ISO-8859-1")

Basic understanding

accommodation["Category"].unique()

array(['hostel', 'hotel', 'motel'], dtype=object)

• There are basically three categories of stay available
  • hostel
  • hotel
  • motel

Hostel stays

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'

data = [
    go.Scattermapbox(
        lat=accommodation[accommodation['Category']=="hostel"]['Latitude '],
        lon=accommodation[accommodation['Category']=="hostel"]['Longitude'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=accommodation[accommodation['Category']=="hostel"]['Name'],
    )
]





layout = go.Layout(title="Hostel locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=12
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'

data = [
    go.Scattermapbox(
        lat=accommodation[accommodation['Category']=="hotel"]['Latitude '],
        lon=accommodation[accommodation['Category']=="hotel"]['Longitude'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=accommodation[accommodation['Category']=="hotel"]['Name'],
    )
]





layout = go.Layout(title="Hotel locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=9
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

• There is a cluster of hotel stays available near beach area of fort kochi

mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'

data = [
    go.Scattermapbox(
        lat=accommodation[accommodation['Category']=="motel"]['Latitude '],
        lon=accommodation[accommodation['Category']=="motel"]['Longitude'],
        mode='markers',
        marker=dict(
            size=7
        ),
        text=accommodation[accommodation['Category']=="motel"]['Name'],
    )
]





layout = go.Layout(title="motel locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

Cultural activities

cat=cultural['Category'].unique()

cultural.head()

	Name	Category	Latitude	Longitude
0	Dance and Arts Center	Arts Centre	9.944677	76.340799
1	Greenix Ventures	Arts Centre	9.968512	76.249920
2	St Mary's Convent Auditorium	Arts Centre	9.964851	76.244722
3	Art Gallery	Artwork	9.960486	76.258944
4	Chavara Statue	Artwork	9.993676	76.357279

def funct(cat):
    data=[]
    for i in cat:
        trace=go.Scattermapbox(
                lat=cultural[cultural['Category']==i]['Latitude '],
                lon=cultural[cultural['Category']==i]['Longitude'],
                mode='markers',
                marker=dict(
                    size=7
                ),
                text=cultural[cultural['Category']==i]['Name'],name=i
            )
        data.append(trace)
    return data



data=[]
data=funct(cat)

layout = go.Layout(title="cultural activities locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

cat=Gastronomy["Category"].unique()

def funct(cat):
    data=[]
    for i in cat:
        trace=go.Scattermapbox(
                lat=Gastronomy[Gastronomy['Category']==i]['Latitude'],
                lon=Gastronomy[Gastronomy['Category']==i]['Longitude'],
                mode='markers',
                marker=dict(
                    size=7
                ),
                text=Gastronomy[Gastronomy['Category']==i]['Name'],name=i
            )
        data.append(trace)
    return data


data=[]
data=funct(cat)

layout = go.Layout(title="Gastronomy locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

cat=Recreation['Category'].unique()

def funct(cat):
    data=[]
    for i in cat:
        trace=go.Scattermapbox(
                lat=Recreation[Recreation['Category']==i]['Latitude'],
                lon=Recreation[Recreation['Category']==i]['Longitude'],
                mode='markers',
                marker=dict(
                    size=7
                ),
                text=Recreation[Recreation['Category']==i]['Name'],name=i
            )
        data.append(trace)
    return data


data=[]
data=funct(cat)

layout = go.Layout(title="Recreation activities locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')

cat=religious['Category'].unique()

def funct(cat):
    data=[]
    for i in cat:
        trace=go.Scattermapbox(
                lat=religious[religious['Category']==i]['Latitude'],
                lon=religious[religious['Category']==i]['Longitude'],
                mode='markers',
                marker=dict(
                    size=7
                ),
                text=religious[religious['Category']==i]['Name'],name=i
            )
        data.append(trace)
    return data


data=[]
data=funct(cat)

layout = go.Layout(title="Religious centre locations around kochi",
    autosize=True,
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,style="streets",
        center=dict(
            lat=9.96,
            lon=76.25
        ),
        pitch=0,
        zoom=10
    ),
)

fig = dict(data=data, layout=layout)

plotly.offline.iplot(fig, filename='Montreal Mapbox')